Cranes play a key role in maintaining the economic vitality of modern-day industry. Their importance can be seen at shipyards, construction sites, warehouses, and in a wide variety of material-handling applications. The effectiveness of crane manipulation is an important contributor to industrial productivity, low production costs, and worker safety. Unfortunately, one inherent property of conventional crane assemblies that is detrimental to efficient operation is the natural tendency for the payload to oscillate like a pendulum, a double-pendulum, or with hoist-related oscillatory dynamics. Because crane operators can only drive the overhead crane trolley—not the payload—there is a response delay from the time the trolley moves to the time the payload moves. This delay results in oscillations in the payload as the trolley slows down (suddenly) or stops moving. The oscillating payload can be very dangerous to the payload, as it may collide with surroundings, or workers in the area. In conventional crane control systems, this delay causes cranes that contain rotational joints an especially challenging control problem because their nonlinear dynamics create additional complexities.
Significant efforts have been made to develop crane control systems that reduce the oscillatory response from both issued commands and external disturbances. Researchers have explored crane problems using neural networks and optimal control. There have also been developments in varying degrees of crane automation. Unfortunately, in addition to facing the challenges of controlling large amplitude, lightly-damped payload swing, operators of conventional crane control systems must also master non-intuitive machine interfaces, which require extensive training. Therefore, expert crane operators typically require years of experience and training. Some examples of conventional non-intuitive crane interfaces include push-button pendants, joysticks, and control levers.
FIG. 1 illustrates a conventional crane control system using a push button pendent interface. The operator must be adept in the cognitive process of transferring the desired manipulation path into a sequence of button presses that will produce the desired motion of the crane trolley 105. For example, if the operator wants to drive the payload 115 through a cluttered workspace using a push-button pendent 120, then the desired path must be mapped into a sequence of events where the “Forward”, “Backward”, “Left”, and “Right” buttons are pushed for the correct time duration and in the correct sequence. Furthermore, as operators move through the workspace to drive the payload 115 and monitor its progress, they may rotate their bodies and change the directions they are facing. In such cases, the orientation of the buttons changes as the operators rotate their bodies. For example, the “Forward” button can cause relative motion to the left, right, or even backward. As an additional challenge, the operator can only directly drive the crane trolley 105, not the payload 115. Therefore, the operator must account for the time lag between the commanded motion of the crane trolley 105, which can be many meters overhead, and the delayed oscillatory response of the payload 115.
While significant strides have been made to improve the operational efficiency of cranes by controlling the dynamic response to issued commands, relatively little consideration has been given to the way in which operators issue those commands. Thus, there is a desire for crane control systems that allow an operator to intuitively issue control commands to a crane that result in minimal payload oscillations, such that the directional crane movement commands are unaffected by the operators changing rotational orientation.